testprog1.c

/*
 * testprog1.c
 * 
 * Copyright 2011 Fernando Pujaico Rivera <fernando.pujaico.rivera@gmail.com>
 * 
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 * 
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 * 
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
 * MA 02110-1301, USA.
 * 
 */

#include <stdlib.h>
#include <math.h>
#include <pds/pdsra.h>
#include <pds/pdsrv.h>
#include <pds/pdsnn.h>

#include "octplot.h"
#include "data_work.h"
    
int main(int argc, char** argv)
{
        PdsNeuronML *MLayer=NULL;
        PdsVector *L=NULL;
        PdsVector *X=NULL;
        PdsVector *Y=NULL;
        PdsUniform *RV=NULL;

        PdsNnNatural i,id,itera,iterb;
    PdsNnNatural Ntraining=128;
    PdsNnNatural Ntests=4;
        PdsNnNatural MaxIter;
        PdsNnReal Error;
        PdsNnReal Alpha;

        // Creo una Random Variable
        RV=pds_uniform_new(-1,1);

        // Creo el vector que indicará la cantidad de electrones por capa.
        // 5 Capas.
    PdsRaReal Larray[]={32,64,42,28,14,7};
        L=pds_vector_new_array1d(Larray,6);

        // Creo El vector de entrada X.
        X=pds_vector_new(Larray[0]);
        // Creo El vector de entrada Y.
        Y=pds_vector_new(Larray[5]);


        MLayer=pds_neuronml_new(L);
        pds_neuronml_init_weight_uniform(MLayer,RV);
        pds_neuronml_init_u_uniform(MLayer,RV);
        Alpha=0.6;      pds_neuronml_set_alpha(MLayer,Alpha);
        Error=0.1;      pds_neuronml_set_max_error(MLayer,Error);
        MaxIter=8192;   pds_neuronml_set_max_iterations(MLayer,MaxIter);

        // Entrenando
    id=0;
        for(i=0;id<Ntraining;i++)
        {
                printf("\nEntrenamiento %4d: id=%4d\n",i,id);

                // Entrenando de forma negativa.
        function_data_work_init_data0(X,Y);
                pds_neuronml_training(MLayer,X,Y);
                itera=pds_neuronml_training_results_printf(MLayer,"-1");

                // Entrenando de forma positiva.
        function_data_work_init_data1(X,Y);
                pds_neuronml_training(MLayer,X,Y);
                iterb=pds_neuronml_training_results_printf(MLayer,"+1");
                
        if( (itera==0)&&(iterb==0) )    id++;
        else                            id=0;

        }

        printf("\n\n");
        // Iterando
        for(i=0;i<Ntests;i++)
        {
        function_data_work_init_data1(X,Y);
                pds_neuronml_iterate(MLayer,X,Y);
                pds_neuronml_iterate_results_printf(MLayer,"+1");
        function_octplot_vector_in_png(X,"n","X[n]","octfilegood.m","xgood.png");
        }


        printf("\n");
        for(i=0;i<Ntests;i++)
        {
        function_data_work_init_data0(X,Y);
                pds_neuronml_iterate(MLayer,X,Y);
                pds_neuronml_iterate_results_printf(MLayer,"-1");
        function_octplot_vector_in_png(X,"n","X[n]","octfilebad.m","xbad.png");
        }
    
        pds_neuronml_free(MLayer);

        printf("\n");

        pds_vector_free(X);
        pds_vector_free(Y);
        pds_vector_free(L);

        return EXIT_SUCCESS;
}